{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-the-type-and-target-of-offensive","title":"Predicting the Type and Target of Offensive Posts in Social Media","arxiv_id":"1902.09666","date":"2019-02-25","proceeding":"NAACL 2019 6","authors":["Marcos Zampieri","Shervin Malmasi","Preslav Nakov","Sara Rosenthal","Noura Farra","Ritesh Kumar"],"abstract":"As offensive content has become pervasive in social media, there has been\nmuch research in identifying potentially offensive messages. However, previous\nwork on this topic did not consider the problem as a whole, but rather focused\non detecting very specific types of offensive content, e.g., hate speech,\ncyberbulling, or cyber-aggression. In contrast, here we target several\ndifferent kinds of offensive content. In particular, we model the task\nhierarchically, identifying the type and the target of offensive messages in\nsocial media. For this purpose, we complied the Offensive Language\nIdentification Dataset (OLID), a new dataset with tweets annotated for\noffensive content using a fine-grained three-layer annotation scheme, which we\nmake publicly available. We discuss the main similarities and differences\nbetween OLID and pre-existing datasets for hate speech identification,\naggression detection, and similar tasks. We further experiment with and we\ncompare the performance of different machine learning models on OLID.","url_abs":"http://arxiv.org/abs/1902.09666v2","url_pdf":"http://arxiv.org/pdf/1902.09666v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-the-type-and-target-of-offensive","repo_url":"https://github.com/joeykay9/offenseval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"language-identification","task_name":"Language Identification"},{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[],"datasets_introduced":[{"slug":"olid","name":"OLID","full_name":"Offensive Language Identification Dataset"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.09666","atlas_url":"https://app.syntology.ai/?focus=1902.09666","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}